Research Interests:
Research Interests:
The exponential growth of electrical demand and the integration of renewable energy sources (RES) brought new challenges in the traditional grid about energy quality. The transition from traditional grid to smart grid is the best solution... more
The exponential growth of electrical demand and the integration of renewable energy sources (RES) brought new challenges in the traditional grid about energy quality. The transition from traditional grid to smart grid is the best solution which provides necessary tools and information and communication technologies (ICT) for service enhancement. In this study, variation of energy demand and some factors of atmospheric change are considered to forecast production of photovoltaic energy that can be adapted for evolution of consumption in smart grid. The contribution of this study concerns a novel optimized hybrid intelligent model made of the artificial neural network (ANN), support vector machine (SVM), and particle swarm optimization (PSO) implemented for long term photovoltaic (PV) power generation forecasting based on real data of consumption and climate factors of the city of Douala in Cameroon. The accuracy of this model is evaluated using the coefficients such as the mean squar...
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Research Interests:
Research Interests:
Smart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State... more
Smart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State estimation uses a processing algorithm based on data from smart meters. The major challenge for state estimation is to take into account this large volume of measurement data. In this article, a novel smart distribution network state estimation algorithm has been proposed. The proposed method is a combined high-gain state estimation algorithm named adaptive extended Kalman filter (AEKF) using extended Kalman filter (EKF) and unscented Kalman filter (UKF) in order to achieve better intelligent utility grid state estimation accuracy. The performance index and the error are indicators used to evaluate the accuracy of the estimation models in this article. An IEEE 37-node test network is used to implement the state estimation models. The state variables cons...
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Research Interests:
Research Interests:
Research Interests:
Fault detection is crucial in smart grid control and monitoring operations. The use of smart meters leads to appearance of a large amount of digital data whose conventional and chronological techniques are not efficient enough for... more
Fault detection is crucial in smart grid control and monitoring operations. The use of smart meters leads to appearance of a large amount of digital data whose conventional and chronological techniques are not efficient enough for processing and decision-making. In this paper, a novel data analysis model based on deep learning and neuro-fuzzy algorithm is proposed for detection and classification of faults in a smart grid. First, the Long Short Term Memory (LSTM) based deep learning model is applied for training the data samples extracted from the smart meters. Then, the Adaptive Neuro Fuzzy Inference System (ANFIS) is implemented for fault detection and classification from the trained data. With this intelligent method proposed, single-phase, two-phase and three-phase faults can be identified using a restricted amount of data. To verify the effectiveness of our methodology, an intelligent model of the IEEE 13-node network is used. The results indicate that the combined ANFIS-LSTM d...